System and method of estimating spectral contributions in ambient light and correcting field of view errors

ABSTRACT

The present disclosure describes systems, methods, and devices for estimating spectral contributions in ambient light. The present disclosure also describes systems, methods, and devices for compensating for field of view errors resulting from the user, contextual structures (e.g., buildings, trees, fixtures, or geological formations), atmospheric effects (e.g., ozone coverage, smog, fog, haze, or clouds), device structures, and/or device orientation/tilt relative to a light source being measured (e.g., sun, indoor/outdoor light emitter, or an at least partially reflective surface). The present disclosure also describes systems, methods, and devices for estimating spectral contributions in light or color measurements and accounting for field of view errors to obtain a refined estimate.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit under 35 U.S.C. §119(e) ofU.S. Provisional Application Ser. No. 62/033,280, entitled SYSTEM ANDMETHOD OF ESTIMATING SOLAR SPECTRAL CONTRIBUTIONS IN AMBIENT LIGHT,filed Aug. 5, 2014. U.S. Provisional Application Ser. No. 62/033,280 ishereby incorporated by reference in its entirety.

BACKGROUND

Ultraviolet (UV) light affects the human body in both beneficial (e.g.,vitamin D and tanning) and detrimental (e.g., skin wrinkling, skincancer, and sun burn or erythema) ways. UV light is typically moredifficult to measure than visible and near infrared light because thespectral content is much weaker than visible light and the shortwavelength provides an abundance of challenges for detection systems. Inboth UV and visible light systems optical filters are typically anglesensitive, where the passband shifts to shorter wavelengths at higherangles of incidence, limiting the useful field of view of a sensor.Solutions are desirable for converting a measured spectrum to a desiredspectrum by compensating for the difference between a measured andtarget spectrum as well as the difference in desired field of view.

The UV spectrum is made up of three regions: UVA, UVB and UVC. Solar UVCradiation is blocked by the earth's atmosphere. Solar UVB light ispartially blocked by the stratospheric ozone layer, and UVA lightlargely transmits. Both UVA and UVB light experience significantRayleigh scattering, the phenomenon responsible for making the sky blue.The UVB spectral range (˜280-315 nm) includes shorter wavelengths thanthe UVA spectral range (˜315-400 nm) and is mostly responsible forsunburn, carcinoma of the skin and vitamin D generation. UVA includeslonger wavelengths that cause tanning, freckles and skin aging effects.

The shorter wavelengths of UV light pose challenges for efficientdetection with common photodiode materials. To detect UV light, either aspecial shallow junction photodiode in a typical Optoelectronic materialsuch as silicon can be used, such as a lateral junction on SOI or alower volume supply, wide bandgap material (e.g., SiC or AlGaN). In thiscontext, measuring UVB is much more challenging than measuring UVA. Mostoptical window or lens materials are highly or partially transmissive toUVA. Few are highly transmissive to UVB, and they are usually moreexpensive. Additionally UVA is 20% bandwidth and UVB is 10% bandwidth,which makes optical filter design more challenging for UVB and moresusceptible to angle-dependency. Additionally, the filter layers arethinner and thickness control tolerances rapidly become critical to acostly degree. Lastly and most importantly, there is very little UVB inthe solar spectrum at any given time, approximately 0-4% of the total UVradiation depending on the atmospheric conditions. For example, FIG. 5shows a spectral photocurrent response of a lateral junction photodiodewith visible blocking filter overlaid with a solar spectrum. As can beseen in FIG. 5, the UVB spectral contributions to the detected light aremuch lower than the UVA spectral contributions.

UVB is not only hard to measure with a detector; it is also challengingto manufacture a UVB detector in a cost-effective manner due to thetight tolerances needed for filter response, dopant profile, field ofview, surface states, strain, and the like. Poor responsivity fornarrower bandgap detectors like silicon is compounded by higher darkcurrent. In addition, manufacturing issues abound for wide bandgapsemiconductors, particularly dislocation density and yield. In bothcases the requisite large area and preference for a diffuser to limitangle-sensitivity of the optical filter magnify the detector sizes andsystem cost. Trimming and/or calibrating a UVB detector also requires aUVB light source (preferably broadband), such as a Xenon lamp. Theselight sources tend to be large, bulky costly, noisy, and highmaintenance.

Finally, (unless heavily diffused) a typical optical detector has afield of view (FOV) limited by the optical package. UV Index is definedfor an ideal planar detector (e.g., having at least 120 degree FOV). Amethod is needed to relate narrow FOV (e.g., ≦90 degrees) UVA or totalUV measurements to what would be measured by a wide FOV UVB or erythemaaction spectrum weighted detector. A high accuracy solution forestimation of biologically relevant spectral contributions (e.g.,human-health relevant UV Index or CIE 1931 XYZ color values) is alsodesirable with a manufacturable detection system for mobile consumerapplications, among others.

SUMMARY

The present disclosure describes systems, methods, and devices forestimating spectral contributions in ambient light. The presentdisclosure also describes systems, methods, and devices for compensatingfor field of view errors resulting from the user, contextual structures(e.g., buildings, trees, fixtures, or geological formations),atmospheric effects (e.g., ozone coverage, smog, fog, haze, or clouds),device structures, and/or device orientation/tilt relative to a lightsource being measured (e.g., sun, indoor/outdoor light emitter, or an atleast partially reflective surface). The present disclosure alsodescribes systems, methods, and devices for estimating spectralcontributions in light or color measurements and accounting for field ofview errors to obtain a refined estimate.

In some implementations, a method of estimating spectral contributionsin ambient light includes: receiving a light measurement of a mobiledevice, the light measurement having a spectral response; receiving alocation and a time associated with the light measurement of the mobiledevice; providing a spectral correction factor appropriate to thelocation and the time associated with the light measurement of themobile device; and scaling the light measurement of the mobile device bythe spectral correction factor to obtain a target measure.

In some implementations, a method of correcting field of view errorsaffecting light or color measurements includes: receiving a light orcolor measurement of a mobile device, the light or color measurementbeing associated with a first field of view; detecting light within twoor more differing fields of view; and scaling the light or colormeasurement of the mobile device with a correction factor based upon thedetected light within the two or more differing fields of view.

One or more of the methods described above may be manifested as a systemfor estimating spectral contributions in ambient light. In someembodiments, the system may include a first sensor configured to detectlight within a first field of view. One or more processors incommunication with the first sensor may be configured to: receive alight measurement via the first sensor, the light measurement having aspectral response; receive a location and a time associated with thelight measurement of the first sensor; provide a spectral correctionfactor appropriate to the location and the time associated with thelight measurement of the first sensor, and scale the light measurementof the first sensor by the spectral correction factor to obtain a targetmeasure.

In some embodiments, the system may include a second sensor configuredto detect light within a second field of view different from a firstfield of view of the light measurement. The system may further include athird sensor configured to detect light within a third field of viewdifferent from the first and second fields of view. The one or moreprocessors may be communicatively coupled with the second and thirdsensors and may be configured to scale the light measurement of thefirst sensor with a field of view correction factor based upon thedetected light within the second and third fields of view. In someembodiments, the system can further include additional sensors (e.g.,fourth, fifth, and so forth) with differing fields of view, where thecorrection factor is based on measurements from some or all of thesecondary sensors.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DRAWINGS

The Detailed Description is described with reference to the accompanyingfigures. The use of the same reference numbers in different instances inthe description and the figures may indicate similar or identical items.

FIG. 1 is a block diagram illustrating a system for estimating spectralcontributions in ambient light in accordance with an embodiment of thepresent disclosure.

FIG. 2 is a flow diagram illustrating a method of estimating spectralcontributions in ambient light in accordance with an embodiment of thepresent disclosure.

FIG. 3 is a flow diagram illustrating a method of correcting field ofview errors affecting measured spectral contributions in ambient lightin accordance with an embodiment of the present disclosure.

FIG. 4 is a flow diagram illustrating a method of estimating spectralcontributions in ambient light and correcting field of view errorsaffecting the measured spectral contributions in accordance with anembodiment of the present disclosure.

FIG. 5 is a graph showing spectral photocurrent response of a lateraljunction photodiode with visible blocking filter overlaid with a solarspectrum.

FIG. 6 is a graph showing an exemplary solar spectrum from space (AM0)and at the earth's surface (AM1.5), relative to an ideal 5800K blackbodyemitter.

FIG. 7 is a graph showing an exemplary responsivity curve, withresponsivity R(λ) [A/W] on the y-axis, versus wavelength λ [nm] on the xaxis.

FIG. 8 is a graph showing the human sunburn (erythema) susceptibilityspectrum.

FIG. 9 illustrates an exemplary environment including one or morestructures that block, reflect, or scatter light.

FIG. 10 is a graph showing UV Index measured with a sensor systemimplemented in accordance with this disclosure (e.g., sensor systemshown in FIG. 1) and a reference meter with respect to the time of theday.

FIG. 11 is a plot showing annual behavior of mean ozone levels bylatitude and month.

FIG. 12A illustrates solar rays propagating within an environmentsubstantially free of cloud cover.

FIG. 12B illustrates solar rays propagating within an environmentaffected by cloud cover.

FIG. 13 is a block diagram illustrating a system for estimating spectralcontributions in ambient light in accordance with an embodiment of thepresent disclosure, wherein the system further includes at least twosecondary light sensors for detecting light within additional fields ofview different from the field of view of the primary sensor.

FIG. 14 illustrates solar rays propagating relative to the system ofFIG. 13 within an environment substantially free of cloud cover.

FIG. 15 illustrates solar rays propagating relative to the system ofFIG. 13 within an environment affected by cloud cover.

FIG. 16 is a block diagram illustrating a system for estimating spectralcontributions in ambient light in accordance with an embodiment of thepresent disclosure, wherein the system includes a cover windowpositioned above a primary light sensor of the system.

FIG. 17 is a block diagram illustrating a system for estimating spectralcontributions in ambient light in accordance with an embodiment of thepresent disclosure, wherein a mobile handset provides data inputs to anapplication driver to determine a spectral correction factor.

FIG. 18 is a graph showing an exemplary response curve for gesturephotodiode detector.

FIG. 19 is a block diagram illustrating a system for estimating spectralcontributions in ambient light in accordance with an embodiment of thepresent disclosure, wherein the system further includes at least foursecondary light sensors for detecting light within additional fields ofview different from the field of view of the primary sensor.

FIG. 20 is a flow diagram illustrating a method of correcting for angleor field of view errors affecting a light or color sensor measurement inaccordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

Overview

Ultraviolet (UV) light is broken into several spectral bands. UVCradiation is blocked by the earth's atmosphere, while the atmospheretransmits the UVB (˜280-315 nm) and UVA (˜315-400 nm) spectral bands toa varying degree. A typical solar spectrum is shown in FIG. 6,illustrating an exemplary solar spectrum from space (AM0) and at theearth's surface (AM1.5), relative to an ideal 5800K blackbody emitter.Traditionally measurement of UV, including UV Index has beenchallenging, with even best-in-class scientific instrumentationdelivering only ±5-10% accuracy. This is due in part to the smallUV-to-visible ratio in sunlight, the use of exotic wide bandgapmaterials available at lower volumes than ubiquitous silicon detectors,extreme requirements for sensor spectral accuracy including slopecontrol and extinction ratio, lack of stable high power and broadbandlight sources, and a host of critical system integration issuesincluding sensor field of view. Sensor field of view dependence plays acritical role in color measurement as well.

According to various embodiments of the present disclosure, a cross-cutapproach to UV index measurement is enabled utilizing sensor fusion andan elegant single wideband UV sensor approach. This UV Index sensingsolution removes complexity from the hardware by integrating intelligentalgorithms that accurately predict and account for variation in solarspectrum and intensity across the globe, at all altitudes and in everyseason, and a hardware solution for dynamically increasing the effectivefield of view (FOV) on demand. By combining the capability of varioussensors that are often already in mobile devices with the reliable, costeffective, and ultra-high volume manufacturable, low part variationwideband UV sensor solution, the disclosed systems and methods enablemobile device manufactures (e.g., smartphone, tablet, notebook,smartwatch, media player manufactures, and the like) to provide a UV orvisible light sensor with accuracy approaching scientific quality fororders of magnitude less cost and size.

In some embodiments, spectral contributions are determined by weightingmeasured UV counts according to theoretical models and contextualinformation including, but not limited to, one or more of: time,location, altitude, orientation, pressure, and ozone mapping. Forexample, the following is considered. UVA represents the majority95-100% of the solar UV content and is most responsible for skin effectssuch as tanning and aging (wrinkles). UVB at the earth's surfacecomprises only a few percent of the total UV content in sunlight(varying with time of day), yet UVB is most responsible for sunburn andcancer due to the ionizing nature of the higher energy photons which candamage DNA. The output signal of some UV sensors, such as the MAX86902sensor, include contributions from both UVA and UVB. Time and locationinformation (and/or other contextual information) can be used inconjunction with a spectral response model to determine how much of thesignal is due to UVA, UVB, or a similar biologically-relevant metriclike UV Index.

Example Implementations

FIGS. 1 through 19 illustrate various embodiments and implementations ofsystems, methods, and devices for estimating spectral contributions inambient light and/or correcting field of view errors affecting lightmeasurements. Those skilled in the art will appreciate that theembodiments illustrated in the drawings and/or described herein may befully or partially combined to result in additional embodiments.Accordingly, the illustrated and described embodiments should beunderstood as explanatory and not as limitations of the presentdisclosure.

FIG. 1 shows an embodiment of a system 100 including at least one sensor102 configured to detect light within a first FOV 104. One or moreprocessors 106 in communication with the sensor 102 may be configured toexecute program instructions 110 stored by at least one carrier medium108 to carry out a process of estimating spectral contributions based ondetected light (e.g., measured UV counts) by the sensor 102 andcontextual information retrieved from or determined via acommunicatively coupled local or remote device (e.g., GNSS, timer,altimeter, accelerometer or other motion sensor, pressure sensor, or thelike) and/or retrieved from a local or remote database. For example,contextual information may be retrieved via direct link or wireless link(e.g., IP protocol, satellite-based communication, radio-basedcommunication, cell phone tower, Bluetooth beacon, or the like).Possible location determination architectures include, but are notlimited to: a separate position sensor in communication with the one ormore processors 106, where a location determination algorithm is run bythe one or more processors 106; a separate position sensor incommunication and a state machine (e.g., programmable logic device) witha hard coded location determination algorithm; an integrated sensor andan ASIC; an integrated sensor and an ASIC with the ASIC and the one ormore processors 106 each running a portion of the location determinationalgorithm; or a separate sensor and front end processor that feespartially processed (e.g., pre-processed) data to the one or moreprocessors 106.

The one or more processors 106 may be configured to receive a lightmeasurement via the sensor 102, the light measurement having a spectralresponse. To enable intensity scanning and determination of relative sunposition, the sensor 102 may be swept across a plurality of positionsand/or orientations such that multiple FOVs are captured. In someembodiments, the sensor 102 is included in a mobile device (e.g., mobilephone, tablet, media player, smartwatch, notebook computer, or thelike). By way of example, the measured spectral response may include a(non-ideal) spectral response based on wideband UV or visiblemeasurements. The one or more processors 106 may be further configuredto provide a spectral correction factor appropriate to contextualinformation (e.g., time and location) associated with the lightmeasurement of the sensor 102. In some embodiments, the spectralcorrection factor is based upon a target spectrum relative to thespectral response of the light measurement of the sensor 102 accordingto a light model appropriate to the contextual information. The targetspectrum may include, but is not limited to, UV index, human erythemaaction spectrum, vitamin D action spectrum, UVA, UVB, carcinoma index, aparticular photobiological action spectrum, a human eye color responsespectrum (red, green, blue) or color mapping such as XYZ, LUV or LAB,bilirubin photolysis action spectrum, photosynthesis action spectrum,material aging spectrum, sterilization action spectrum, orphotocatalytic action spectrum. The one or more processors 106 may beconfigured to weight the light measurement of the sensor 102 by thespectral correction factor to obtain a target measure. In someembodiments, the one or more processors 106 are further configured toaggregate multiple measurements (possibly at different locations,different angles, or another controlled variable) and/or contextual datainputs to improve accuracy and granularity of the target measure.

An exemplary response curve of the sensor 102 is shown in FIG. 7. Thesensor response [ADC counts] is the product of the solar irradiance S(λ)[Wcm⁻²nm⁻¹], sensor area Ar=8.4 e-4 cm², the ADC gain conversion G(counts/A), sensor responsivity R(λ) [A/W], and any additional windowtransmission T(λ) effects summed over the range of sensor wavelengths.UV counts=G·Ar∫ ₂₈₀ ⁴⁰⁰ dλT(λ)S(λ)R(λ)

One advantage of the wideband UV for system integration is that avariety of common phone cover glass materials provide substantial UVAtransmission (T(λ)≈0.94), such that the sensor readout is fairlyinsensitive to the material and thickness of the cover glass or coverlens material.

The spectral intensity of the sunlight can be expressed in a simplifiedmodel in terms of the relative distance traveled through the atmosphere(relative to 90 degrees transmission straight down). This amount ofatmosphere that sunlight travels through is called “air mass” AM.S(x,y,z,AM,λ)˜e ^(−α(λ)AM(x,y,z))

In an advanced model the atmospheric extinction coefficient α(λ) isderived by considering absorption, mostly from ozone in the upperatmosphere, and Rayleigh scattering from molecules and particles in thelower atmosphere. Given α(λ), it is possible to obtain a series of solarspectra for different amounts of air mass corresponding to solarirradiance at earth's surface at different times of the day. Acalculated spectrum is only as general as the solar spectrum it is basedon. Local regions have extinction coefficients that differ due to airpressure and seasonal ozone levels. For example, a plot of average ozonelevels by latitude and month is shown in FIG. 11. In addition,appropriately ratioed contributions of the unique direct and diffuselight spectra are considered to compensate for differing fields of viewof the sensor 102 and an ideal reference meter such that satisfactorycorrelation is achieved throughout the day and at varied altitudes.

UVA, UVB and UV Index are ideal measures of integrated intensity fromsubsets of the solar spectrum.UVA=∫₃₁₅ ⁴⁰⁰ dλS(λ), UVB=∫₂₈₀ ³¹⁵ dλS(λ), UV Index=0.04∫₂₅₀ ⁴⁰⁰dλAS(λ)S(λ)

AS(λ) is the human Erythema (sunburn) action spectrum, a piecewisefunction of wavelength [nm], defined below and plotted in log-scale inFIG. 8.

${{AS}(\lambda)} = \left\{ \begin{matrix}{1,} & {250 < \lambda < 298} \\{10^{0.094{({298 - \lambda})}},} & {280 < \lambda < 328} \\{10^{0.015{({139 - \lambda})}},} & {328 < \lambda < 400}\end{matrix} \right.$

The calculated UV Index, for a solar spectrum in units of mW/m², may beobtained by multiplying by 0.04 to get a number that falls in thedefined 0-15 UV index range. AS(λ) heavily weights high energy UVB rays,such that the UV Index contribution of a typical solar spectrum is about87% UVB. The UV Index may be accurately detected by either: (1)measuring the total intensity with the precise responsivity defined byAS(λ), or (2) knowing both the light spectrum and total intensity from aknown responsivity sensor.

A traditional UV sensor uses method (1) of measuring the total intensitywith the precise responsivity defined by AS(λ) to directly detect UVIndex. However, a dedicated UVB or UV Index sensor alone does notaccurately detect changes in the UVA wavelengths that relate to agingand tanning which appeal to beauty and fashion-conscious consumers andcan be challenging and more costly to manufacture and test in highvolume.

The approach manifested by system 100 relates to method (2) of knowingboth the light spectrum and total intensity from a known responsivitysensor. This approach utilizes a broadband UV measurement to gathertotal intensity, and a combination of sensor data and accurate solarmodels to obtain the typical solar spectrum based on contextualinformation such as, but not limited to, location, elevation, pressure,time of day, and/or time of year. One advantage of this approach is thata simple detector, such as the sensor 102, can leverage existinginfrastructure in a mobile handset and a flexible approach that allowsestimation of particular UV measures, such as UVA, UVA, UV Index, humancarcinoma index, and the like. This approach relies on proven spectralcalculation tools developed for NASA and NOAA projects and is supportedby extensive global field test validation data. Because the solarspectrum is primarily a function of ozone concentration, angle of thesun in the sky, air pressure (altitude) and the time of day, the sensor102 may include a simple wideband UV sensor supplemented by retrievabledata inputs and predetermined models. For example, an algorithm (e.g.,ANDROID, IOS, or WINDOWS framework-based algorithm) may use phonelocation services (e.g. GPS, Wi-Fi, cell tower triangulation, countrycode and/or time zone information), clock and/or pressure sensor data toobtain the estimated solar spectrum, and from that spectrum derive aspectral correction factor to relate wideband UV measurements to avariety of biologically relevant parameters, such as UVA, UVB, UV Index(sunburn), and human squamous cell carcinoma (skin cancer) risk curves.The sensor 102 measures UV counts. The algorithm determines how many ofthose counts come from each UV spectral region, based on the localinstantaneous solar spectrum. The spectral correction factor (CF)relates the UV counts to the UV index.UV Index(calculated)=CF·UV Counts(measured)

In some embodiments, the extended value of the wideband UV measurementis realized with an atmospheric transmission model that estimates thesolar spectrum at a particular geo-position and time. Given a widebandUV measure and reference solar spectrum, the relative values of spectralsubsets (e.g., UVA, UVB, UV index, carcinoma index) can be estimated.The solar spectrum calculation takes into account factors including, butnot limited to, one or more of: latitude, longitude, altitude, sensorfield of view (e.g., direct vs. diffuse spectrum), time of year, andtime of day. The accuracy of the estimated UVB or UV index depends onhow closely the atmospheric transmission matches the model. Latitudinalor local variations in ozone, and to a lesser degree pressure, humidity,and temperature, can also affect the instantaneous accuracy of theatmospheric model.

In some embodiments, the spectral correction Factor (CF) may be based oncalculated ideal UV Index and calculated sensor response for theexpected solar spectrum.

${CF} = \frac{{UV}\mspace{14mu}{Index}\mspace{14mu}({estimated})}{{UV}\mspace{14mu}{counts}\mspace{14mu}({estimated})}$

The correction factor can be used to scale the UV counts measured by thesensor 102.UV Index(calculated)=CF·UV Counts(measured)

The correction factor may be based on calculated ideal UV Index andcalculated sensor response for the expected solar spectrum. However, thecorrection factor may be calculated by alternative criteria and, ingeneral, is based at least partially on the estimated second spectralresponse (i.e., spectral response based on modeling and contextualinformation).

An astronomical calculation can be used to find the solar zenith angle(angle from vertical) from position and time information. Altitude maybe obtained from a mobile device barometer or OPS service. Thecorrection factor may be determined by the one or more processors 106(e.g., in an ANDROID, IOS, WINDOWS, or other mobile or personalcomputing framework) using pre-compiled data tables such as the annualmean ozone concentration by latitude and time of year (as shown in FIG.10). It is noted however, that any of the functions described herein maybe performed by one or more local device processors, cloud-basedprocessing, and/or a mixture of local and remote processors.Accordingly, any mention of “the one or more processors 106” should beunderstood as including a single processor, a combination of one or morelocal processors, one or more remote processors, hard coded logic,and/or one or more programmable devices.

FIG. 2 illustrates a method 200 of estimating spectral contributions inambient light in accordance with system 100. The method 200 may includethe following steps, and in some embodiments, the method 200 may furtherinclude steps for carrying out one or more of the operations describedherein with regard to system 100. At step 202, the method 200 includesreceiving a light measurement of a mobile device, the solar measurementhaving a spectral response. At step 204, the method 200 includesreceiving a location and a time, and in some embodiments, othercontextual information (e.g., altitude, orientation, ozone mapping data,and/or pressure). At step 206, the method 200 includes providing aspectral correction factor appropriate to the location and timeassociated with the light measurement of the mobile device. At step 208,the light measurement is weighted by the spectral correction factor toobtain a target measure. Accordingly, solar contributions at particularwavelengths or ranges of spectra can be estimated to gain accurateinformation. For example, the estimated spectral contributions mayinclude information regarding light intensity at biologically-relevantspectra. This information may be further paired with associated risks,advisory information, and the like.

An ideal UV index sensor has a cosine angular response which is theresponse of an ideal flat detector. This type of detector typically hasa large dome-shaped plastic diffuser mounted over the top, providing afull width at half maximum (FWHM) FOV of 120 degrees. This extremelywide FOV captures light from the entire sky and provides accuracy in allweather conditions ranging from direct sunlight at high altitude todiffuse, omnidirectional light under thick clouds. However, a domediffuser and its form factor are not attractive for embeddedapplications in mobile devices (e.g., cell phones, tablets, notebooks).Furthermore a wide FOV sensor is quite sensitive to changes in light atwide angles, such as the distance to a user's head, which blocks someindirect light from reaching the sensor (resulting in a “shadow effect”)and can lead to user confusion as they attempt to move the sensor toobtain a stable reading.

A narrow FOV is less sensitive to nearby objects like a user's body,tree, or building, thus resulting in better accuracy for mobile devices.For narrow FOV (e.g., ≦90 degrees), the sensor 102 should be orientedtowards the sun for an accurate reading. However, in cloudy and partlycloudy situations a narrow FOV sensor misses some indirect light andwill measure lower than a reference meter. This is true for all sensorswith limited FOV whether UVA, UVB, UV Index or wideband UV. A balancebetween narrow FOV for improved UV index and wide FOV for accuracy maybe achieved with the first sensor by using secondary sensors (e.g., asshown in FIG. 13) to estimate and compensate for FOV-based error incloudy conditions. FOV correction may be further applied to compensatefor FOV-based error resulting from objects, people, and/or devicestructures (e.g., cover window or reflective surfaces).

The advantage of using a first sensor 102 with a narrow FOV isillustrated in FIG. 9, where the system 100 is included in a mobiledevice. In the exemplary environment 500, obstacles such as structure502 do not significantly affect the measurement accuracy of the firstsensor 102 as long as they are not within the first FOV 104. In othercontexts, an FOV correction factor (e.g., cloud correction factor “CC”)may be used to scale the UV counts measurement collected by the firstsensor 102.UV Index(calculated)=CF·UV Counts(measured)*CC

Limited FOV reduces UV signal captured in cloudy conditions relative tothe wide angle reference meter. Detection and correction is needed forhigh accuracy in haze, clouds, fog, or similar light scatteringconditions. FIGS. 12A and 12B illustrate the difference between UVsignal captured in sunny and cloudy conditions. A reference meter has amuch larger FOV and is capable of capturing scattered light in cloudyand non-cloudy conditions. Whereas, the first sensor 102 may have anarrow FOV (e.g., less than 90°) that excludes portions of scatteredlight.

As shown in FIG. 13, the system 100 may include one or more secondarylight sensors to detect presence of scattered light outside of the firstFOV 104. For example, the system 100 may include a second sensor 112configured to detect light within a second FOV 114 that is differentfrom the first FOV 104. The system 100 may further include a thirdsensor 116 configured to detect light propagating within a third FOV 118that is different from the first FOV 104 and the second FOV 114. In someembodiments, the second sensor 112 and the third sensor 116 may bepartially covered or shaded by one or more shielding structures 120(e.g., metal shields). In some embodiments, the secondary sensors 112and 116 may include photo-detectors exhibiting different spectralresponsivity than the first sensor 102. For example, the secondarysensors 112 and 116 may be configured to detect a range of visible lightspectra, while the first sensor 102 is configured to detect a range ofUV light spectra. The one or more processors 106 may be communicativelycoupled with the second sensor 112 and the third sensor 116 and may beconfigured to weight the light measurement of the first sensor 102 witha FOV correction factor based upon the detected light within the secondFOV 114 and the third FOV 118.

When no clouds are detected (i.e., light detected in the second FOV 114and the third FOV 118 is insubstantial), no FOV correction is applied tothe UV signal captured by the first sensor 102. For example, FIG. 14includes a schematic representation of the light shielding structures120 on the first sensor 102 which receive primarily light incident athigh angles. The first sensor 102 may include silicon photodiodes withbroadband, wide angle response. The secondary sensors (i.e., the secondsensor 112 and the third sensor 116) may include broadband siliconphotodiodes as well; however, the angular response function of eachsecondary sensor peaks away from zero.

When clouds are detected, UV rays may be scattered in random directions.This may cause the first sensor 102 to detect signals at highconcentrations but not account for significant portions of scatteredlight that are not within the first FOV 104. For example, FIG. 15illustrates the randomness of UV ray scattering that may occur in cloudyconditions. Based on the levels of light detected within the second FOV114 and the third FOV 118 by the second sensor 112 and the third sensor116, respectively, relative to the level of light detected by the firstsensor 102, the one or more processors 106 may determine a FOVcorrection factor. The FOV correction factor may be scaled to accountfor cloud cover or for other light scattering, reflecting, or blockingsurfaces. For example, light scattering, reflecting, or blockingsurfaces may include, but are not limited to, fog, haze, smog, smoke,rain, snow, natural or manmade structures, people, animals, vehicles, ordevice structures.

Some device structures are known to scatter and/or reflect light atcertain incident angles. For example, as shown in FIG. 16, a coverwindow 122 may be positioned adjacent to (e.g., above) the first sensor102. This configuration may result in reflectance or scattering of lightat certain angles. To compensate for the lost signal (i.e., undetectedportions of light), the FOV correction factor can also be scaled tocompensate for effects of the cover window 122 and/or other lightscattering or reflecting device structures.

FIG. 18 shows an exemplary angular response curve of a secondary sensor(e.g., sensor 112 or sensor 116). In an exemplary implementation, theangular response curve of the first sensor 102 (not shown) may peak atnormal incidence and may have approximately cosine-like response withinpackage FOV. For light centered on-axis, the ratio of light leveldetected by the secondary sensors to the light level detected by thefirst sensor 102 is approximately unity. For light with large angularextent (e.g., due to clouds), the ratio increases. The FOV correctionfactor (e.g., cloud correction factor “CC”) may be calculated based onthe effective FOV calculated as the ratio of the shielded secondarysensors 112 and 116 (SS) to the unshielded first sensor (FS). That is,FOV=SS/FS*100, with threshold parameter (T) and slope (M).

${CC} = \left\{ \begin{matrix}{1,} & {{{if}\mspace{14mu}{FOV}} < T} \\{{1 + {M\left( {{FOV} - T} \right)}},} & {{{if}\mspace{14mu}{FOV}} \geq T}\end{matrix} \right.$

In sunny conditions, the effective FOV may be in the range ofapproximately 0.9-1.45. The reading from the first sensor 102 may bemore accurate when the light source angular extent is well within thefirst FOV 104. When the light source extends beyond the sensor first FOV104 (e.g., effective FOV>threshold), correction is needed. In partlycloudy conditions, the effective FOV may increase to approximately 2.Due to package FOV limitations, in extremely cloudy conditions theeffective FOV returns to 1 and the error due to clouds may remain notcompensated. However, in these conditions the UV Index is quite low,typically less than 3, and overall error remains less than 1 UV Indexpoints. Exemplary algorithm settings at sea level can include, but arenot limited to, T=1.45 and M=0.75. The threshold (T) may be adjustedautomatically with altitude to compensate the decreasing angularsubtense of the sun in the sky.

FIG. 3 illustrates a method 300, in accordance with various embodimentsof system 100 described above, of correcting field of view errorsaffecting measured spectral contributions in ambient light. The method300 may include the following steps, and in some embodiments, the method300 may further include steps for carrying out one or more of theoperations described herein with regard to system 100. The method 300may be applied to correct FOV errors (i.e., compensate for limited FOV)in a light measurement of a mobile device. At step 302, the method 300includes detecting light within a second FOV different from a first FOVof the light measurement of the mobile device. At step 304, the method300 includes detecting light within a third FOV different from the firstand second FOVs. At step 306, the method 300 includes weighting thelight measurement of the mobile device with a FOV correction factorbased upon the detected light within the second FOV and third FOVs.

FIG. 4 illustrates a method 400, in accordance with various embodimentsof system 100 described above, of estimating spectral contributions inambient light and correcting field of view errors affecting measuredspectral contributions in ambient light. At step 402, the method 400includes measuring UV counts (e.g., using a first sensor 102). In someembodiments, the first sensor 102 measures counts proportional to thephotocurrent generated from solar radiation on a custom UV sensor diewith an integral high efficiency visible blocking filter. At step 404,the method 400 includes determining a spectral correction factor (e.g.,as described above with regard to system 100 and method 200). In someembodiments, contextual information (e.g., atmospheric pressure,location, altitude, time, or the like) is retrieved by a processor 106that then provides a spectral correction factor which determines howmany of the measured counts are due to light under a target spectrum(e.g., UV Index or erythema action spectrum). At step 406, the method400 includes determining a FOV correction factor based on light detectedin one or more secondary FOVs (e.g., as described above with regard tosystem 100 and method 300). In some embodiments, the one or moreprocessors 106 determine to what degree clouds are present frommeasurements and compensate for differences between the measurementscollected via the first sensor 102 and an ideal (cosine) detector, whicheffectively widens the FOV to improve accuracy in cloudy, partly cloudyconditions, and other light scattering, reflecting, or blockingconditions. At step 408, the method 400 includes weighting the measuredUV counts with the spectral correction factor and the FOV correctionfactor to obtain a target measure, where limited FOV of the mobiledevice sensor is accounted for to improve measurement accuracy.

In some embodiments, the system 100 can further include additionalsensors (e.g., fourth, fifth, and so forth) with differing fields ofview, where the correction factor is based on measurements from some orall of the secondary sensors. For example, an embodiment of the system100 is shown in FIG. 19, where the first sensor 102 forms a centerchannel and additional sensors 112, 113, 115, and 116 form left, up,down, and right channels, respectively. The light intensity detected byeach of the secondary sensors (e.g., sensors 112, 113, 115, and 116) canbe normalized by dividing each channel measurement by the sum of thesecondary channel measurements or by dividing each channel measurementby light intensity detected by the center channel 102 or by anotherlight sensor near the secondary sensors. Any of the sensorconfigurations described herein can be applied to measuring spectra orother characteristics of any light source (not only solar measurements).For example, the system configurations and methods described herein canalso be used to measure spectral contributions from fluorescent,incandescent, LED sources, and the like. Moreover, angle-dependentweighting and correction of measurements can be extended to colorcorrection. For example, light detected at secondary sensors can be usedto determine a color correction factor that can be applied to colorspectral measurements (e.g., the center channel 102 may be a colorsensor array).

In some implementations, the FOV correction factor can be calculated tocorrect for an error in the centration of the light source in thedetector field of view (e.g., cases where most of the light is comingoff-axis where the sensor is less responsive than on-axis). The FOVs ofsecondary detectors may allow for a differential reading. For example,left 112, right 116, up 113 and down 115 facing detectors normalized bythe sum of the responses (or by a central detector 102 or another nearbydetector) can allow for differential estimation of the light sourceangle. In some implementations, the FOV correction factor calculationmay include a measurement of the device's angle relative to the sun.This angle is calculated from difference of the known angle of the sun,based on time and location information, and the angle of the mobiledevice from the earth surface normal, which may be measured with a 3Daccelerometer and a compass incorporated into the mobile device.

FIG. 20 illustrates a method 700, in accordance with various embodimentsof system 100 described above, of correcting for angle or field of viewerrors affecting a light or color sensor measurement. At step 702, ameasurement is received from a sensor 102 (e.g., photodetector, UV lightsensor, color sensor array, or the like) with angle-dependentsensitivity or spectral characteristics. At step 704, measurements arereceived from secondary sensors (e.g., left, up, down, and right sensors112, 113, 115, and 116) having differing fields of view from one anotherand from the central sensor 102. In some implementations, the method 700optionally includes (step 706) determining angle offset of the lightsource (e.g., sun or another single light source). For example, theangle offset can be determined by calculating (L−R)/C and/or (U−D)/C,where L, R, U, D, and C are the left, right, up, down, and centralchannel measurements, respectively. The method 700 can also optionallyinclude (step 708) estimating light source spectrum or characteristicsbased on contextual information (e.g., location, time, tilt angle,etc.), or in color sensor array implementations, the method 700 caninclude receiving a preliminary (uncorrected) spectrum or colorcorrelated temperature (CCT) reading. At step 710, angular extent (e.g.,FOV or angle of view (AOV)) of the sensor measurement is determinedbased upon the secondary sensor measurements. For example, the angularextent can be determined by calculating (L+R+U+D)/C. This works for bothsingle light source measurements and for multiple light sourcemeasurements. At step 712, an FOV or AOV correction factor is calculatedbased on a lookup table (e.g., 2D table correction factor) compared witha CCT reading, or based on a calculated correction factor compared withsolar spectrum and detected FOV. At step 714, the sensor measurement isscaled by the FOV or AOV correction factor. The method 700 can alsooptionally include (step 716) recalculating the light source spectrum orother characteristics based on the corrected sensor measurement.

In some implementations, secondary sensors (e.g., sensors 112, 113, 115,and 116) can also be used to detect and account for obstacles such aspeople, buildings, cars, clouds, or any other structural orenvironmental condition that can block light from reaching the sensorsas would be expected. For example, the system 100 can be configured tocompare the respective measurements of the sensors with one another andwith an expected measurement based on a previous measurement, ameasurement collected by another (nearby) user, or an expectedmeasurement based upon modeling with contextual information (e.g., time,location, altitude, orientation, etc.). When an object is detected, themeasurement can be corrected with mathematical modeling, anothermeasurement can be collected, a user may be notified that an obstacle isaffecting the sensor measurement, and/or the user can be advised toreposition the sensors for a more appropriate reading.

In some implementations, solar exposure can be tracked without constantmonitoring of the sun. Sunlight changes quickly during the day,especially at the shorter wavelengths where transmission through theatmosphere is particularly sensitive to solar angle. UV Index, forexample, between 1-3 hours before or afternoon the UV Index goes up fromalmost negligible amounts to very close to the daily maximum. So asingle measurement of the sun may not be accurate for tracking exposure.Accordingly, continuous monitoring can be desired. The challenge withlight monitoring with a mobile device is that the mobile device must bein constant view of the sun, which isn't practical for phones becausethey are usually put into a pocket or purse and can get hot if left outin the sun. Accordingly, time (T0) and location (L) information can beused to calculate, look up, generate or select an appropriate lightintensity curve versus time of day. Because the local solar irradiancedepends on albedo, local buildings, trees and especially, ozoneconcentration, clouds and fog, and the like, the intensity curve is notaccurate for all locations but can provide a theoretical value. Inimplementations, a method takes or receives a light intensitymeasurement S. The curve is then scaled by the ratio S/E(T0,L) which isthen locally accurate for the prediction of the light for short periodsof time, or long periods of time if environmental conditions and/orlocation are not rapidly changing. The exposure is calculated as theintegral of the scaled expected intensity curve. Measurements at latertimes (T1, T2, etc.) can be used to rescale the curve to provideimproved accuracy. However, because the light intensity changes soquickly and often predictably, the scaled curve can provide improvedaccuracy over a point-wise integration. Notably, a single measurementmay suffice for a significant portion of the day if conditions are notrapidly changing. The light intensity measurement can be a directmeasurement or inferred measurement using time and location informationas described herein. This method then functions as auser-location-specific predictive extrapolation, which can be used toset timer limits for UV exposure for tanning or sunburn avoidance. Thismethod can further benefit from the device knowing whether it is indoorsor outdoors in the case the user is moves indoors temporarily, so thatthe integration can be stopped when the user with a mobile device in apocket goes indoors, for example, and then restarted when the user isoutdoors. In this regard, the method can also include a selectiveintegration based on user location (e.g., indoor or outdoor position).

Mobile devices can have additional advantages for light monitoringbecause they are often connected to a network. In some implementations,a method is provided for light intensity tracking and mapping, whereindividual light intensity measurements are aggregated and madeavailable to mobile devices or for mobile device users. When users makemeasurements they can be reported to a server which updates a local UVintensity map, atmospheric gas concentration map, or the like. Spatialinterpolation can be used to improve density mapping accuracy for users.Additionally, for predictable measurements (e.g., light characteristicsor spectra), predictive extrapolation can also be used. In this manner,a mobile device that is in a user's pocket can integrate a user'spotential UV exposure without the user having to take the mobile deviceout of the user's pocket, but rather, by using interpolated UV Indexlevels from a reading served by a nearby user. In the absence of asuitable reading, a pure prediction can be used. In someimplementations, the server or a local/remote processor may selectivelydisallow readings from the map that are peculiar using Bayesiantechniques in order to improve overall accuracy. The server or alocal/remote processor may weight readings from devices that tend to beclose to the average (i.e., “good” representative users) or are morefrequent or are the highest. The processing can also be server-based,ad-hoc or peer-to-peer.

FIG. 17 is block diagram illustrating an exemplary implementation ofvarious modules on a mobile device 600 (e.g., mobile handset) that feedone or more application drivers. In embodiments, the mobile device 600includes one or more hardware or software modules (e.g.,clock(s)/timer(s) 602 and/or 608, position detector(s)/receiver(s) 604and/or 606, pressure sensor(s) 610, accelerometer(s), altimeter(s),etc.) configured to detect, determine, or receive contextualinformation, including, but not limited to, one or more of: time, date,location, altitude, orientation, or atmospheric pressure. At least someof the contextual information can be received from a remote server. Forexample, the contextual information can include a mixture of sensor datacollected by one or more sensors of the mobile device 600 and datareceived from a remote server. Some contextual information may be atleast partially based on or can be adjusted or selected according to thesensor data and/or data received from a remote server. For example,sensor data (e.g., time, date, location, altitude, orientation, and/oratmospheric pressure) can be fed into an ozone level table 612 or asolar angle calculator 614, or other contextual information database oralgorithm running on the device 600 or at a remote server. In thisregard, an application can be run entirely on the device 600, or can becloud based, or partially run on-device and partially run on the cloud.The mobile device 600 may include at least one processor or controllerconfigured to feed the contextual information into one or moreapplication drivers, and optionally configured to run one or moreapplications or application software modules on-device. The on-device orremotely operating (e.g., cloud-based) processor(s) are configured togenerate a spectral correction factor (CF) based on the contextualinformation (e.g., time, date, location, altitude, orientation,atmospheric pressure, solar angle, and/or ozone mapping). This spectralcorrection factor can then be applied to a detected spectrum (e.g., UVspectral response) in order to obtain a desired (target) spectralmeasurement.

It should be recognized that the various functions, operations, or stepsdescribed throughout the present disclosure may be carried out by anycombination of hardware, software, or firmware. In some embodiments,various steps or functions are carried out by one or more of thefollowing: electronic circuitry, logic gates, multiplexers, aprogrammable logic device, an application-specific integrated circuit(ASIC), a controller/microcontroller, or a computing system. A computingsystem may include, but is not limited to, a personal computing system,mainframe computing system, workstation, image computer, parallelprocessor, or any other device known in the art. In general, the terms“controller” and “computing system” are broadly defined to encompass anydevice having one or more processors, which execute instructions from acarrier medium.

Program instructions implementing methods, such as those manifested byembodiments described herein, may be transmitted over or stored oncarrier medium. The carrier medium may be a transmission medium, suchas, but not limited to, a wire, cable, or wireless transmission link.The carrier medium may also include a non-transitory signal bearingmedium or storage medium such as, but not limited to, a read-onlymemory, a random access memory, a magnetic or optical disk, asolid-state or flash memory device, or a magnetic tape.

It is further contemplated that any embodiment of the disclosuremanifested above as a system or method may include at least a portion ofany other embodiment described herein. Those having skill in the artwill appreciate that there are various embodiments by which systems andmethods described herein can be implemented, and that the implementationwill vary with the context in which an embodiment of the disclosure isdeployed.

Furthermore, it is to be understood that the invention is defined by theappended claims. Although embodiments of this invention have beenillustrated, it is apparent that various modifications may be made bythose skilled in the art without departing from the scope and spirit ofthe disclosure.

What is claimed is:
 1. A method of estimating the spectral contributionsin ambient light, comprising: receiving a light measurement from a firstsensor of a mobile device, the light measurement having a spectralresponse; receiving a location and a time associated with the lightmeasurement; providing a spectral correction factor appropriate to thelocation and the time associated with the light measure; scaling thelight measurement by the spectral correction factor to obtain a targetmeasure; detecting light within two or more differing fields of viewwith at least one secondary sensor of the mobile device, wherein the twoor more differing fields of view are at least partially defined by atleast one shielding structure disposed between the first sensor and theat least one secondary sensor, wherein the at least one shieldingstructure covers a partial portion of a sensor area of the at least onesecondary sensor; and scaling the light measurement with a correctionfactor based upon the light detected within the two or more differingfields of view, wherein the correction factor is determined based onlevels of the light detected within respective ones of the two or morediffering fields of view relative to a level of light detected within afirst field of view of the first sensor.
 2. The method of claim 1,wherein the spectral correction factor is based upon a target spectrumrelative to the spectral response of the light measurement, according toa light model appropriate to the location and the time associated withthe light measurement.
 3. The method of claim 2, wherein the targetspectrum comprises at least one of: an UV index, a visible colorspectrum, human erythema action spectrum, a vitamin D action spectrum,an UVA spectrum, an UVB spectrum, a carcinoma index, a photobiologicalaction spectrum, a photosynthesis action spectrum, a material agingspectrum, a sterilization action spectrum, or a photocatalytic actionspectrum.
 4. The method of claim 2, wherein the time includes a time ofday and a time of year.
 5. The method of claim 2, wherein the lightmodel is further based upon an altitude associated with the lightmeasurement.
 6. The method of claim 2, wherein the light model isfurther based upon an atmospheric pressure associated with the lightmeasurement.
 7. A system for estimating spectral contributions inambient light, comprising: a first sensor configured to detect lightwithin a first field of view; two or more secondary sensors configuredto detect light within two or more differing fields of view, wherein thetwo or more differing fields of view are at least partially defined byat least one shielding structure disposed between the first sensor andat least one secondary sensor of the two or more secondary sensors,wherein the at least one shielding structure covers a partial portion ofa sensor area of the at least one secondary sensor; and one or moreprocessors configured to: receive a light measurement via the firstsensor, the light measurement having a spectral response; receive alocation and a time associated with the light measurement of the firstsensor; provide a spectral correction factor appropriate to the locationand the time associated with the light measurement of the first sensor;scale the light measurement of the first sensor by the spectralcorrection factor to obtain a target measure, wherein the spectralcorrection factor is based upon a target spectrum relative to thespectral response of the light measurement of the first sensor accordingto a light model appropriate to the location and the time associatedwith the light measurement of the first sensor; and scale the lightmeasurement of the first sensor with a correction factor based upon thelight detected within the two or more differing fields of view, whereinthe correction factor is determined based on levels of the lightdetected within respective ones of the two or more differing fields ofview relative to a level of the light detected within the first field ofview.
 8. The system of claim 7, wherein the target spectrum comprises atleast one of: an UV index, a visible color spectrum, human erythemaaction spectrum, a vitamin D action spectrum, an UVA spectrum, an UVBspectrum, a carcinoma index, a photobiological action spectrum, aphotosynthesis action spectrum, a material aging spectrum, asterilization action spectrum, or a photocatalytic action spectrum. 9.The system of claim 7, wherein the time includes a time of day and atime of year.
 10. The system of claim 7, wherein the light model isfurther based upon an altitude associated with the light measurement ofthe first sensor.
 11. The system of claim 7, wherein the light model isfurther based upon an atmospheric pressure associated with the lightmeasurement of the first sensor.